This specification relates to time series analysis.
Time series analyses are important in many fields including, for example, econometrics, finance, weather forecasting, stock price forecasting, and earthquake prediction. One example time series model is autoregressive integrated moving average (“ARIMA”). Autoregressive integrated moving averages can be fitted to time series data to better understand the data or to predict future points in the series. The model typically includes three parameters that refer to the order of the autoregressive, integrated, and moving average parts of the model.